class_docstr: Placeholder docstring
class_docstr_tok: ["Placeholder","docstring"]
code: def __init__(
      self,
      model: Optional[Any],
      schema_builder: SchemaBuilder,
      model_loader_path: Optional[str] = None,
      inference_spec: Optional[InferenceSpec] = None,
      save_path: Optional[str] = None,
      s3_path: Optional[str] = None,
      sagemaker_session: Optional[Session] = None,
      role_arn: Optional[str] = None,
      metadata: Optional[Metadata] = None,
  ):
      self.model = model
      self.schema_builder = schema_builder
      self.model_loader_path = model_loader_path
      self.inference_spec = inference_spec
      self.s3_path = s3_path
      self._update_save_path(save_path)
      self.sagemaker_session = sagemaker_session
      self.py_version = None
      self.role_arn = role_arn
      self.metadata = metadata
  
file_path: sagemaker-python-sdk/src/sagemaker/serve/save_retrive/version_1_0_0/save/save_handler.py
imports: from __future__ import absolute_import
  from typing import Optional, Type, Any
  from dataclasses import dataclass, field
  import datetime
  import logging
  from pathlib import Path
  from sagemaker.serve.save_retrive.version_1_0_0.save.utils import upload_to_s3, capture_dependencies, capture_optimization_metadata, detect_framework_and_its_versions
  from sagemaker import Session
  from sagemaker.model import Model
  from sagemaker.serve.builder.schema_builder import SchemaBuilder
  from sagemaker.serve.spec.inference_spec import InferenceSpec
  from sagemaker.serve.save_retrive.version_1_0_0.metadata.metadata import Metadata
  from sagemaker.serve.save_retrive.version_1_0_0.save.framework.pytorch_handler import PyTorchHandler
  from sagemaker.serve.save_retrive.version_1_0_0.save.framework.xgboost_handler import XGBoostHandler
label_desc: Use image node_id 1 to create a new SaveHandler object  with example: obj = SaveHandler(model, schema_builder, model_loader_path, inference_spec, save_path, s3_path, sagemaker_session, role_arn, metadata)